Biomedical subjects

Mark Chang

Publications and source records attributed to Mark Chang.

5 recordsLinked to original sources

PubMed2007

Adaptive design method based on sum of p-values.

Bauer and Kohne proposed an adaptive design using Fisher's combination of independent p-values based on subsamples from different stages (Biometrics 1994; 50(4):1029-1041). Their method provides great flexibility in the selection of statistical methods for hypothesis testing of subsamples. However, the choices for the stopping boundaries are not flexible enough to meet practical needs (Biometrics 2001; 57(3): 886-891). In this paper, an adaptive design method is proposed using linear combination of the independent p-values. The method provides great flexibility in the selection of stopping boundaries and no numerical integration is required for the two-stage designs. The stopping boundaries and p-values can be calculated manually. The operating characteristics of the adaptive designs are studied using computer simulations with and without sample size adjustment. Examples are presented for superiority and non-inferiority trials with different endpoints (normal, binary, and survival) under different adaptations. The statistical efficiency of the proposed method is compared with other methods based on conditional power.

Data Interpretation, Statistical↗

PubMed2006

Adaptive design in clinical research: issues, opportunities, and recommendations.

The issues and opportunities of adaptive designs are discussed. Starting with the definitions of an adaptive design, its validity and integrity are discussed. The three key components of an adaptive design, i.e., Type I error control, p-value adjustment, and unbiased estimation and confidence interval are addressed. Various seamless designs are investigated. Recommendations are made in the following aspects: study planning, trial monitoring, analysis and reporting, trial simulation, and regulatory perspectives.

Bayes Theorem↗

PubMed2005

Statistical inference for cancer trials with treatment switching.

In cancer clinical trials, it is not uncommon that some patients switched their treatments due to lack of efficacy and/or disease progression under ethical consideration. This treatment switch makes it difficult for the evaluation of the efficacy of the treatment under investigation. The current existing methods consider random treatment switch and do not take into consideration of prognosis and/or investigator's assessment that leads to patients' treatment switch. In this paper, we model patients' treatment switching effect in a latent event times model under parametric setting or a latent hazard rate model under the semi-parametric proportional hazard model. Statistical inference procedures under both models are provided. A simulation study is performed to investigate the performance of the proposed methods.

Antineoplastic Agents↗

PubMed2005

Statistical consideration of adaptive methods in clinical development.

In recent years, the use of adaptive methods in clinical development based on accrued data has become very popular due to its flexibility in modifying trial procedures and/or statistical procedures of on-going clinical trials. However, it is a concern that the actual patient population after the modifications could deviate from the originally targeted patient population. Major modifications of trial procedures and/or statistical procedures of on-going trials may result in a totally different trial, which is unable to address the scientific/medical questions that the trial intends to answer.

Algorithms↗

PubMed2005

A hybrid Bayesian adaptive design for dose response trials.

In recent years, the use of adaptive design methods based on accrued data of on-going trials have become very popular for dose response trials in early clinical development due to their flexibility (EMEA, 2002). In this paper, we developed a hybrid frequentist-Bayesian continual reassessment method (CRM) in conjunction with utility-adaptive randomization for clinical trial designs with multiple endpoints. The proposed hyperlogistic function family with multiple parameters gives users flexibility for probability modeling. CRM reassesses a dose-response relationship based on accrued data of the on-going trial, which allows investigators to make decisions based on a constantly updated dose-response model. The proposed utility-adaptive randomization for multiple-endpoint trials allows more patients to be assigned to superior treatment groups. The performance of the proposed method was examined in terms of its operating characteristics through computer simulations.

Algorithms↗